Grokking Dynamic Programming Interview in C++

The ultimate guide to dynamic programming interviews in C++ with strategies developed by FAANG engineers. Practice with real-world questions and get interview-ready in just a few hours.

5.0

53 Lessons

25h

Updated 1 month ago

Also available in

C++

Java

JavaScript

Python

Also available in

C++

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Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an advanced optimization technique applied to recursive solutions. However, DP is not a one-size-fits-all technique, and it requires practice to develop the ability to identify the underlying DP patterns. With a strategic approach, coding interview prep for DP problems shouldn’t take more than a few weeks. This course starts with an introduction to DP and thoroughly discusses five DP patterns. You’ll learn to apply each pattern to several related problems, with a visual representation of the working of the pattern, and learn to appreciate the advantages of DP solutions over naive solutions. After completing this course, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence. This course is also available in JavaScript, Python, and Java—with more coming soon!

Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an adv...Show More

WHAT YOU'LL LEARN

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets

The ability to identify and apply the underlying pattern in an interview question by assessing the problem statement

Familiarity with dynamic programming techniques with hands-on practice in a setup-free coding environment

The ability to efficiently evaluate the tradeoffs between time and space complexity in different solutions

A flexible conceptual framework for solving any dynamic programming question, by connecting problem characteristics and possible solution techniques

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets

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Frequently Asked Questions

What is dynamic programming, and how does it help in coding interviews?

Dynamic programming (DP) is an optimization technique for solving problems by breaking them into simpler, interdependent subproblems. By solving each subproblem once and storing its result, DP avoids redundant calculations. This approach is crucial for coding interviews because many real-world problems, especially optimization and decision-making, rely on DP to find efficient solutions.

What are some common dynamic programming patterns I should know for interviews?

Some common DP patterns frequently tested in interviews are as follows:

0/1 knapsack: Used for selection problems with constraints.
Unbounded knapsack: Useful when you can use an item multiple times.
Recursive numbers: Includes problems like Fibonacci, path counting, and combinatorial problems.
Longest common substring/subsequence: Involves finding similarities or transformations between sequences.
Palindromic subsequence: Focuses on identifying and analyzing palindrome-related problems.

Why is dynamic programming emphasized in technical interviews?

Interviewers focus on dynamic programming because it tests your ability to break complex problems into smaller, manageable parts. DP questions challenge candidates to think critically, optimize for efficiency, and use resources effectively—skills highly valued in software engineering roles.

How can dynamic programming proficiency enhance my performance in coding interviews?

Being skilled in dynamic programming equips you to tackle a broad range of optimization problems and shows your ability to solve problems efficiently. As DP is frequently used in interview questions, mastering it will increase your chances of impressing interviewers, particularly at top companies like FAANG, where problem-solving ability is key.

How should I explain a dynamic programming solution in an interview?

To effectively explain your dynamic programming approach, identify the brute-force solution and its limitations. Then, explain how DP reduces these limitations by solving and storing subproblems. Walk the interviewer through your thought process—whether memoization or tabulation—ensuring you explain key decisions about space and time complexity optimization.

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